UAV Swarm Tracking Method Based on Wide-Area Deployment of Intelligent Reflecting Surfaces

doi:10.12382/bgxb.2022.0217

Abstract

Abstract:

Unmanned Aerial Vehicles (UAVs) play an important role in both civil and military domains. However, their small size, large quantity, and high speed pose significant security threats to national defense. Ensuring low-altitude safety requires effective tracking and locating of UAVs. A cost-effective target tracking method is thus proposed for tracking multiple targets. By deploying low-cost intelligent reflectors across a wide area, data fusion of multiple targets is performed. An improved data association method is proposed. Through feature-assisted fuzzy data association, a part of historical data is used as the feature threshold to screen the optimal observation data, and the measured data that is closest to the real value is obtained. Finally, Kalman filter is used for state estimation to realize the tracking of multiple targets with low cost and high precision. The performance of the proposed method is compared with that of the traditional probability density data association algorithm. The results show that the proposed algorithm achieves smaller root mean square error in position and speed, with a tracking accuracy of around 1.7m, while the traditional algorithm is about 6.6m. Experimental results verify that the proposed method can effectively improve the target association accuracy and tracking performance.

Key words: unmanned aerial vehicle, multi-target tracking, wide-area deployment, intelligent reflecting surface, data association, fuzzy clustering

ZHENG Lei, CHEN Zhimin, JIA Yuxuan. UAV Swarm Tracking Method Based on Wide-Area Deployment of Intelligent Reflecting Surfaces[J]. Acta Armamentarii, 2023, 44(6): 1837-1845.

Figures/Tables 9

References 23

[1]	张云飞, 林德福, 郑多, 等. 多目标时空同步协同攻击无人机任务分配与轨迹优化[J]. 兵工学报, 2021, 42(7): 1482-1495.
	ZHANG Y F, LIN D F, ZHENG D, et al. Task allocation and trajectory optimization of uav for multi-target time-space synchronization cooperative attack[J]. Acta Armamentarii, 2021, 42(7): 1482-1495. (in Chinese) doi: 10.3969/j.issn.1000-1093.2021.07.016
[2]	屈旭涛, 庄东晔, 谢海斌. “低慢小”无人机探测方法[J]. 指挥控制与仿真, 2020, 42(2): 128-135. doi: 10.3969/j.issn.1673-3819.2020.02.024
	QU X T, ZHUANG D Y, XIE H B. Detection methods for low-slow-small (lss) uav[J]. Command Control & Simulation, 2020, 42(2): 128-135. (in Chinese)
[3]	WU Q Q, ZHANG R. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 1995, 58(1): 106-112.
[4]	LI Z F, HUA M, WANG Q X, et al. Weighted sum-rate maximization for multi-IRS aided cooperative transmission[J]. IEEE Wireless Communications Letters, 2020, 9(10): 1620-1624. doi: 10.1109/LWC.5962382 URL
[5]	LÜ B, HOANG D T, GONG S, et al. IRS-based wireless jamming attacks: when jammers can attack without power[J]. IEEE Wireless Communications Letters, 2020, 9(10): 1663-1667. doi: 10.1109/LWC.5962382 URL
[6]	HUM S V, PERRUISSEAU-CARRIER J. Reconfigurable reflectarrays and array lenses for dynamic antenna beam control: a review[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(1): 183-198. doi: 10.1109/TAP.2013.2287296 URL
[7]	ASADCHY V S, ALBOOYEH M, TCVETKOVA S N, et al. Perfect control of reflection and refraction using spatially dispersive metasurfaces[J]. Physical Review B, 2016, 94(7): 1-14.
[8]	ESTAKHRI N M, ALU A. Wave-front transformation with gradient metasurfaces[J]. Physical Review X, 2016, 6(4): 041008. doi: 10.1103/PhysRevX.6.041008 URL
[9]	SONG C, WANG B N, XIANG M S. et al. A general framework for slow and weak range-spread ground moving target indication using airborne multichannel high-resolution radar[J]. Transactions on Geoscience and Remote Sensing, 2022, 60: 5113616.
[10]	杨万海. 多传感器数据融合及其应用[M]. 西安: 西安电子科技大学出版社, 2004.
	YANG W H. Multisensor data fusion and its application[M]. Xi’an: Xidian University Press, 2004. (in Chinese)
[11]	李康, 丁国如, 李京华, 等. 无源定位技术发展动态及其应用分析[J]. 航空兵器, 2021, 28(2): 104-112.
	LI K, DING G R, LI J H, et al. Development and application analysis of passive localization[J]. Aero Weaponry, 2021, 28(2): 104-112. (in Chinese)
[12]	CAO L, ZHENG D Y, ZHAO Z M, et al. Convex variational inference for multi-hypothesis fractional belief propagation based data association in multiple target tracking systems[J]. Sensors Journal, 2021, 21(17):19121-19133. doi: 10.1109/JSEN.2021.3089206 URL
[13]	CHAKRABORTY B, LI Y, ZHANG J J, et al. Multipath exploitationwith adaptivewaveform design for tracking in urban terrain[C]//Proceedings of 2010 IEEE International Conference on Acoustics Speech & Signal Processing. Dallas, TX,US: IEEE, 2010: 11540984.
[14]	ZETIK R, ESCHRICH M, JOVANOSKA S, et al. Looking behind a corner using multipath-exploitingUWB radar[J]. IEEE Transactions on Aerospace & Electronic Systems, 2015, 51(3): 1916-1926.
[15]	何友, 修建娟, 张晶炜, 等. 雷达数据处理及应用[M]. 第2版. 北京: 电子工业出版社, 2013.
	HE Y, XIU J J, ZHANG J W, et al. Radar data processing and application[M]. 2nd ed. Beijing: Publishing House of Electronics Industry, 2013. (in Chinese)
[16]	邹汝平, 刘建书. 基于概率假设密度滤波与无迹Kalman滤波的多目标跟踪与识别[J]. 兵工学报, 2020, 41(8):1502-1508. doi: 10.3969/j.issn.1000-1093.2020.08.004
	ZOU R P, LIU J S. Multi-target tracking and recognition technology based on PHD and UKF[J]. Acta Armamentarii, 2020, 41(8):1502-1508. (in Chinese) doi: 10.3969/j.issn.1000-1093.2020.08.004
[17]	邢慧, 颜景龙, 张树江. 基于Kalman滤波的稳像技术研究[J]. 兵工学报, 2007, 28(2): 175-177.
	XING H, YAN J L, ZHANG S J. Digital image stabilization using kalman filtering[J]. Acta Armamentarii, 2007, 28(2): 175-177. (in Chinese)
[18]	SITTLER R W. An optimal data association problem in surveillance theory[J]. IEEE Transactions on Military Electronics, 1964, 8(2): 125-139. doi: 10.1109/TME.1964.4323129 URL
[19]	LI X R, BAR-SHALOM Y. Tracking in clutter with nearest neighbor filters: analysis and performance[J]. IEEE Transactions on Aerospace Electronic Systems, 1996, 32(3): 995-1010. doi: 10.1109/7.532259 URL
[20]	KIRUBARAJAN T, BAR-SHALOM Y. Probabilistic data association techniques for target tracking in clutter[J]. Proceedings of the IEEE, 2004, 92(3): 536-557. doi: 10.1109/JPROC.2003.823149 URL
[21]	BICANIC B, MARKOVIC I, PETROVIC I. Multi-target tracking on riemannian manifolds via probabilistic data association[J]. Signal Processing Letters, 2021, 28:1555-1559. doi: 10.1109/LSP.2021.3099980 URL
[22]	MA L, ZHAN R H, ZHANG J. Feature aided tracking algorithm based on generalized probability data association[C]//Proceedings of 2009 International Radar Conference. Guilin, China: IET, 2009.
[23]	赵峰. 特征辅助的多目标数据关联算法研究[D]. 长沙: 国防科学技术大学, 2010.
	ZHAO F. Research on feature-aided data association algorithm for multi-target[D]. Changsha: National University of Defense Technology, 2010. (in Chinese)

目标	x/m	y/m	v_x/(m·s^-1)	v_y/(m·s^-1)
1	3800	2800	15	-8
2	3800	2500	15	8

目标	x/m	y/m	v_x/(m·s^-1)	v_y/(m·s^-1)
1	3800	2800	15	-8
2	3800	2500	15	8

算法	目标	位置/m		速度/(m·s^-1)
算法	目标	x轴方向	y轴方向	x轴方向	y轴方向
特征辅助 FDA算法	1	0.304	0.486	0.447	1.420
特征辅助 FDA算法	2	0.435	1.978	3.186	0.907
PDA算法	1	8.587	5.106	0.709	3.727
PDA算法	2	6.054	4.173	8.485	1.794

算法	目标	位置/m		速度/(m·s^-1)
算法	目标	x轴方向	y轴方向	x轴方向	y轴方向
特征辅助 FDA算法	1	0.304	0.486	0.447	1.420
特征辅助 FDA算法	2	0.435	1.978	3.186	0.907
PDA算法	1	8.587	5.106	0.709	3.727
PDA算法	2	6.054	4.173	8.485	1.794

算法	跟踪精度/m		关联时间/s
算法	目标1	目标2	关联时间/s
特征辅助的FDA算法	1.698 2	1.747 2	0.038 5
PDA算法	7.198 4	6.041 3	0.007 0